A-SDLC Synthetic E-commerce Dataset

This repository provides a small synthetic e-commerce dataset along with helper scripts for generating CSVs, loading them into SQLite, and running analytical queries.

Project Structure

• data/ – generated CSV files (users.csv, products.csv, orders.csv, order_items.csv, payments.csv) plus generate_data.py.
• ingest.py – creates ecommerce.db, defines tables, and loads the CSV files using pandas.
• query.sql – join query combining all entities.
• run_query.py – executes query.sql against ecommerce.db and prints the result set.

Prerequisites

• Python 3.8+
• pip install pandas

Usage

1. Generate data (optional if CSVs already exist)

   python data/generate_data.py

2. Load data into SQLite

   python ingest.py

   This creates/overwrites ecommerce.db in the project root and prints Ingestion complete.
3. Run the join query

   python run_query.py

   Outputs the joined table defined in query.sql.

Customization

• Adjust record counts or value ranges by editing data/generate_data.py.
• Modify query.sql to explore other questions, then re-run run_query.py.

License

This dataset and scripts are provided for demonstration and testing purposes. Use freely within your projects.***